Iatrogenic Opioid Withdrawal in Critically Ill Patients: A Review of Assessment Tools and Management
Bibliographic record
Abstract
OBJECTIVE: To (1) provide an overview of the epidemiology, clinical presentation, and risk factors of iatrogenic opioid withdrawal in critically ill patients and (2) conduct a literature review of assessment and management of iatrogenic opioid withdrawal in critically ill patients. DATA SOURCES: We searched MEDLINE (1946-June 2017), EMBASE (1974-June 2017), and CINAHL (1982-June 2017) with the terms opioid withdrawal, opioid, opiate, critical care, critically ill, assessment tool, scale, taper, weaning, and management. Reference list of identified literature was searched for additional references as well as www.clinicaltrials.gov . STUDY SELECTION AND DATA EXTRACTION: We restricted articles to those in English and dealing with humans. DATA SYNTHESIS: We identified 2 validated pediatric critically ill opioid withdrawal assessment tools: (1) Withdrawal Assessment Tool-Version 1 (WAT-1) and (2) Sophia Observation Withdrawal Symptoms Scale (SOS). Neither tool differentiated between opioid and benzodiazepine withdrawal. WAT-1 was evaluated in critically ill adults but not found to be valid. No other adult tool was identified. For management, we identified 5 randomized controlled trials, 2 prospective studies, and 2 systematic reviews. Most studies were small and only 2 studies utilized a validated assessment tool. Enteral methadone, α-2 agonists, and protocolized weaning were studied. CONCLUSION: We identified 2 validated assessment tools for pediatric intensive care unit patients; no valid tool for adults. Management strategies tested in small trials included methadone, α-2 agonists, and protocolized sedation/weaning. We challenge researchers to create validated tools assessing specifically for opioid withdrawal in critically ill children and adults to direct management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".